Bayesian high-dimensional variable selection in non-linear mixed-effects models using the SAEM algorithm - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Conference Papers Year : 2022

Bayesian high-dimensional variable selection in non-linear mixed-effects models using the SAEM algorithm

Abstract

High-dimensional data, with many more covariates than observations, such as genomic data for example, are now commonly analyzed. However, there are few tools for high-dimensional variable selection when the data are observations collected repeatedly on several individuals, and even fewer when the model is nonlinear. Thus, we develop a high-dimensional covariate selection procedure for nonlinear mixed-effects models that are natural models for analyzing this type of data. More precisely, we propose a spike-and-slab variable selection in which we fit using the stochastic approximation version of EM algorithm. Similarly to lasso regression, the set of relevant covariates is selected by exploring a grid of values for the penalization parameter. The proposed approach is much faster than a classical MCMC algorithm and shows very good selection performances on simulated data. Finally, our methodology is applied on growth biological data for selecting relevant genetic markers.
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Dates and versions

hal-04247984 , version 1 (18-10-2023)

Identifiers

  • HAL Id : hal-04247984 , version 1

Cite

Marion Naveau, Guillaume Kon Kam King, Laure Sansonnet, Maud Delattre. Bayesian high-dimensional variable selection in non-linear mixed-effects models using the SAEM algorithm. Journée APPLIBUGS, Jun 2022, Lyon, France. ⟨hal-04247984⟩
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